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ECE MS Thesis Presentation by Kiriaki Rajotte

Friday, November 22, 2019
12:00 pm to 1:00 pm
Floor/Room #: 
AK 218


Electromyogram (EMG) Signal Analysis: Extraction of a Novel EMG Feature and Optimal Root Difference of Squares (RDS) Processing in Additive Noise



Electromyogram signals generated by human muscles can be measured on the surface of the skin and then processed for use in applications such as prostheses control, kinesiology and diagnostic medicine. Most EMG applications extract an estimate of the EMG amplitude, defined as the time-varying standard deviation of EMG, EMGσ. To improve the quality of EMGσ, additional signal processing techniques, such as whitening, noise reduction and additional signal features can be incorporated into the EMGσ processing. Implementation of these additional processing techniques improve the quality of the processed signal but at the cost of increased computational complexity and required calibration contractions.

Three different signal processing techniques were used in an effort to improve the quality of EMGσ. To temporally uncorrelate the data, a fixed whitening filter was developed using the ensemble mean of the power spectrum density of EMG data from an existing data set. The second signal processing technique that was studied focused on the modeling of EMG contractions at rest to optimize noise subtraction. Through the analytical modeling of EMG at rest, the optimal subtraction of the noise variance is to compute the root of the difference between the signal squared and noise variance (RDS). To reduce the variance of traditional EMGσ feature and eliminate the need for calibration contractions, the third technique that was studied was the extraction of a new EMG feature from four individual signal features.


Research Advisor:

Prof. Edward A. Clancy

ECE Department, WPI


Research Committee:

Prof. Xinming Huang

ECE Department, WPI

Prof. Stephen J. Bitar

ECE Department, WPI